# Mastering Data Visualization with Python

Visualize data using pandas, matplotlib and seaborn libraries for data analysis and data science

4.6/5

## Mastering Data Visualization with Python

### What you’ll learn

Understand what plots are suitable for a type of data you have
Visualize data by creating various graphs using pandas, matplotlib and seaborn libraries

### Requirements

Some basic knowledge of Python is expected. However this course does include a quick overview of Python knowledge required for this course.

### Description

This course will help you draw meaningful knowledge from the data you have.Three systems of data visualization in R are covered in this course:A. Pandas    B. Matplotlib  C. Seaborn       A. Types of graphs covered in the course using the pandas package:Time-series: Line PlotSingle Discrete Variable: Bar Plot, Pie PlotSingle Continuous Variable:  Histogram, Density or KDE Plot, Box-Whisker Plot Two Continuous Variable: Scatter PlotTwo Variable: One Continuous, One Discrete: Box-Whisker PlotB. Types of graphs using Matplotlib library:Time-series: Line PlotSingle Discrete Variable: Bar Plot, Pie PlotSingle Continuous Variable:  Histogram, Density or KDE Plot, Box-Whisker Plot Two Continuous Variable: Scatter PlotIn addition, we will cover subplots as well, where multiple axes can be plotted on a single figure.C. Types of graphs using Seaborn library:In this we will cover three broad categories of plots:relplot (Relational Plots): Scatter Plot and Line Plotdisplot (Distribution Plots): Histogram, KDE, ECDF and Rug Plotscatplot (Categorical Plots): Strip Plot, Swarm Plot, Box Plot, Violin Plot, Point Plot and Bar plotIn addition to these three categories, we will cover these three special kinds of plots: Joint Plot, Pair Plot and Linear Model PlotIn the end, we will discuss the customization of plots by creating themes based on the style, context, colour palette and font.

### Overview

Section 1: Introduction

Lecture 1 Study Plan – Please do NOT skip this

Lecture 3 Python Refresher – Part 1

Lecture 4 Python Refresher – Part 2

Lecture 5 Numpy Refresher

Lecture 6 Pandas Refresher

Section 2: Getting Data and Using the Pandas Package to Plot

Lecture 8 Getting Data for Plotting – Part 1

Lecture 9 Getting Data for Plotting – Part 2

Lecture 10 Anatomy of a Figure

Lecture 11 First Plot Using Pandas

Lecture 12 Refining the First Plot

Lecture 13 Line Plot Continued

Lecture 14 Bar Plot

Lecture 15 Box Plot

Lecture 16 Histogram and KDE Plot

Lecture 17 Scatter Plot

Lecture 18 Pie Plot

Lecture 19 Summary of Commonly Used Plots

Section 3: Matplotlib Library for Plots

Lecture 22 Line Plot Part 1

Lecture 23 Line Plot Part 2

Lecture 24 Bar Plot

Lecture 25 Box Plot

Lecture 26 Histogram

Lecture 27 Scatter Plot

Lecture 28 Pie Plot

Lecture 29 Subplots approach – An Introduction

Lecture 30 The First Plot Using Subplots Approach

Lecture 31 Creating a Plot with Two Axes

Lecture 32 Arrow and Annotation on the Plot

Lecture 33 Bar Plot and Pie Plot

Section 4: Seaborn Library for Plots

Lecture 35 Scatter Plot and Histogram

Lecture 36 Seaborn Library for Plotting – Introduction

Lecture 37 Types of Plots in Seaborn

Lecture 38 Scatter Plot using the Seaborn Library – Part 1

Lecture 39 Scatter Plot using the Seaborn Library – Part 2

Lecture 40 Line Plot using the Seaborn Library

Lecture 41 Displot – Part 1 (Histogram, KDE, ECDF and Rug Plots)

Lecture 42 Displot – Part 2 (Histogram, KDE, ECDF and Rug Plots)

Lecture 43 Two Dimensional Displots

Lecture 44 Catplot – Introduction

Lecture 45 Strip Plot and Swarm Plot

Lecture 46 Box Plot and Violin Plot

Lecture 47 Bar Plot and Point Plot

Lecture 48 Joint Plot (Scatter + Histogram)

Lecture 49 Pair Plot (Multiple Scatter + Histogram Plots)

Lecture 50 Regression or Linear Model Plot

Lecture 51 Setting the Plot Styles

Lecture 52 Setting the Plot Context

Lecture 53 Choosing an Appropriate Color Palette

Lecture 54 Setting the Plot Themes

Section 5: Python for Absolute Beginners

Lecture 56 Installing Anaconda

Lecture 57 Jupyter Notebook

Lecture 58 Getting Started with Python

Lecture 59 Variables and Types

Lecture 60 List – Part 1

Lecture 61 List – Part 2

Lecture 62 Dictionary

Lecture 63 Tuple

Lecture 64 Set

Lecture 65 Logical Operators

Lecture 66 Numpy – Part 1

Lecture 67 Numpy – Part 2

Lecture 68 Numpy – Part 3

Lecture 69 Pandas – Series and DataFrame

Lecture 70 Pandas DataFrame

Lecture 71 Importing .csv Files as DataFrame

Lecture 72 Pandas DataFrame – Dealing with Columns

Lecture 73 Pandas DataFrame – Dealing with Rows

Section 6: Bonus Section

Lecture 74 BONUS LECTURE

Data Science, Six Sigma and other professionals interested in data visualization,Professionals interested in creating publication quality plots,Professionals who are not happy with the plots created in MS Excel, and see them as dull and boring

#### Course Information:

Udemy | English | 9h 27m | 3.32 GB
Created by: Sandeep Kumar, ­ Quality Gurus Inc.

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